@Article{ShimabukuroASHDMDMCA:2023:MaLaUs,
               author = "Shimabukuro, Yosio Edemir and Arai, Egidio and Silva, Gabriel 
                         M{\'a}ximo da and Hoffmann, T{\^a}nia Beatriz and Duarte, 
                         Valdete and Martini, Paulo Roberto and Dutra, Andeise Cerqueira 
                         and Mataveli, Guilherme Augusto Verola and Cassol, Henrique 
                         Lu{\'{\i}}s Godinho and Adami, Marcos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Mapping Land Use and Land Cover Classes in S{\~a}o Paulo State, 
                         Southeast of Brazil, Using Landsat-8 OLI Multispectral Data and 
                         the Derived Spectral Indices and Fraction Images",
              journal = "Forests",
                 year = "2023",
               volume = "14",
               number = "8",
                pages = "e1669",
                month = "Aug.",
             keywords = "agriculture, forest, forest plantation, Land Use and Land Cover 
                         (LULC), Linear Spectral Mixing Model (LSMM), pasture, spectral 
                         indices, urban.",
             abstract = "This work aims to develop a new method to map Land Use and Land 
                         Cover (LULC) classes in the S{\~a}o Paulo State, Brazil, using 
                         Landsat-8 Operational Land Imager (OLI) data. The novelty of the 
                         proposed method consists of selecting the images based on the 
                         spectral and temporal characteristics of the LULC classes. First, 
                         we defined the six classes to be mapped in the year 2020 as 
                         forest, forest plantation, water bodies, urban areas, agriculture, 
                         and pasture. Second, we visually analyzed their variability 
                         spectral characteristics over the year. Then, we pre-processed 
                         these images to highlight each LULC class. For the classification, 
                         the Random Forest algorithm available on the Google Earth Engine 
                         (GEE) platform was utilized individually for each LULC class. 
                         Afterward, we integrated the classified maps to create the final 
                         LULC map. The results revealed that forest areas are primarily 
                         concentrated in the eastern region of S{\~a}o Paulo, 
                         predominantly on steeper slopes, accounting for 19% of the study 
                         area. On the other hand, pasture and agriculture dominated 73% of 
                         all S{\~a}o Paulos landscape, reaching 39% and 34%, respectively. 
                         The overall accuracy of the classification achieved 89.10%, while 
                         producer and user accuracies were greater than 84.20% and 76.62%, 
                         respectively. To validate the results, we compared our findings 
                         with the MapBiomas Project classification, obtaining an overall 
                         accuracy of 85.47%. Therefore, our method demonstrates its 
                         potential to minimize classification errors and offers the 
                         advantage of facilitating post-classification editing for 
                         individual mapped classes.",
                  doi = "10.3390/f14081669 View more",
                  url = "http://dx.doi.org/10.3390/f14081669 View more",
                 issn = "1999-4907",
             language = "en",
           targetfile = "forests-14-01669.pdf",
        urlaccessdate = "14 maio 2024"
}